Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Bulletin of Computer Science Research
Rahmad Abdillah
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Perbandingan Akurasi Arsitektur EfficientNet-B0, VGG16, dan Inception V3 Dalam Deteksi Tumor Ginjal Pada Citra CT-Scan Muhammad Fahri; Febi Yanto; Fadhilah Syafria; Rahmad Abdillah
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.670

Abstract

Kidney dysfunction can trigger the development of various diseases, including kidney tumors. Early detection of kidney tumors is very important to increase the effectiveness of treatment and the chances of patient recovery. The use of deep learning technology in medical image classification has become a promising approach, especially in detecting abnormalities in the kidney organ through CT-Scan images. This study compares the performance of three Convolutional Neural Network (CNN) architectures, namely EfficientNet-B0, Inception-V3, and VGG16, in detecting kidney tumors. The dataset used was obtained from the kaggle website, namely CT-scan images with normal and tumor classes and divided by a ratio of training  data and test data of 80:20. The hyperparameter used is Stochastic Gradient Descent (SGD) with a learning rate of 0.001 and 0.0001. The evaluation was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score . According to the test outcomes, the VGG16 model configured with a 0.001 learning rate achieved the highest classification performance, recording 99.46% accuracy, precision, recall, and F1-score.
Evaluasi Keamanan Website Direktori Akademik Menggunakan NIST SP 800-115 Fito Nardian; Rahmad Abdillah; Benny Sukma Negara; Reski Mai Candra
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1044

Abstract

Evaluating the security of web-based academic information systems has become crucial as cyber threats in higher education environments increase. The track record of security incidents in information systems at UIN Sultan Syarif Kasim Riau has prompted an urgent need for preventative action; therefore, the website https://seminar-fst.uin-suska.ac.id, as an active academic service that stores sensitive data, requires a proactive evaluation. Testing used a black-box testing approach through four phases: planning, discovery, attack, and reporting. The results revealed a critical vulnerability in the form of SQL injection in URL parameters, which allows unauthorized database enumeration (MariaDB), thus threatening data confidentiality and integrity. Additionally, medium-level vulnerabilities were discovered, such as the use of an outdated JavaScript library (Moment.js 2.8.1) and misconfiguration of HTTP security headers, including the absence of a Content Security Policy (CSP) and an Anti-CSRF mechanism. Recommendations include prepared statements, strict input validation, updating dependencies, and strengthening security configurations.
Co-Authors Fadhilah Syafria Febi Yanto Fito Nardian Muhammad Fahri Negara, Benny Sukma Reski Mai Candra